Vue.ai

Vue.ai AI

Vue.ai AI

Vue.ai AI

Vue.ai AI

Vue.ai AI

Vue.ai AI

Vue.ai AI

Vue.ai AI

Vue.ai AI

Vue.ai AI

Just open-sourced a protocol + SDK that lets Claude drive your live app (ships as a Claude Code plugin)

https://github.com/BrainBlend-AI/tesseron Just open-sourced a protocol and TypeScript SDK I built mostly with Claude Code. The goal: let Claude (or any MCP client) drive a live application (browser tab, Electron / Tauri desktop app, Node daemon, CLI) by calling typed handlers inside your code, instead of scraping the UI with Playwright or Computer Use. It's called Tesseron. Ships as a Claude Code plugin, so install is one command: /plugin marketplace add BrainBlend-AI/tesseron /plugin install tesseron@tesseron Plugin spawns a small local MCP gateway automatically. Any running Tesseron-instrumented app connects to the gateway over WebSocket and registers its actions. Claude sees those actions as native MCP tools after a six-character claim-code handshake. Minimal SDK shape on the app side: ```ts import { tesseron } from '@tesseron/web'; import { z } from 'zod'; tesseron.app({ id: 'todo_app', name: 'Todo' }); tesseron .action('addTodo') .input(z.object({ text: z.string().min(1) })) .handler(({ text }) => { state.todos.push({ id: newId(), text }); render(); }); await tesseron.connect(); ``` Handlers receive a ctx arg so they can pause mid-run: ctx.confirm({ question }): yes/no, surfaced as a native Claude Code confirmation, not another model turn ctx.elicit({ schema, question }): typed form back from the user ctx.progress({ percent, message }): streaming status while the handler runs ctx.sample({ prompt }): call Claude's LLM inline (generate a commit message from inside a deploy handler, etc.) How Claude helped: roughly 90% of the code was written by Claude Code under review. I drove architecture and API shape (the ctx surface, the Zod-first builder, the claim-code handshake, the protocol spec itself). Claude wrote the bulk implementation, the 65-test suite, the full Starlight docs site, the entire plugin shell, and all 6 framework examples (same todo app in vanilla TS / React / Svelte / Vue / Node / Express). Most recursive moment in the build: using Claude Code to rewrite its own plugin bundle when we cut the protocol from 0.2 to 1.0. v1.0 shipped last week. Reference SDKs on npm for browser, Node, React hooks, and the gateway. Free and open source: BUSL-1.1 on the implementation (free for in-app and self-hosted use, auto-converts to Apache-2.0 after 4 years), protocol spec CC BY 4.0 so anyone can write a compatible client or server in any language. Python and Rust (for Tauri) are on the roadmap. Links: Docs: https://brainblend-ai.github.io/tesseron/ Protocol spec: https://brainblend-ai.github.io/tesseron/protocol/ Repo + 6 worked examples (same todo app in vanilla TS / React / Svelte / Vue / Node / Express): https://github.com/BrainBlend-AI/tesseron submitted by /u/TheDeadlyPretzel [link] [comments]

I made a thing where Claude edits my running app, not code. Drag, resize, type — the diff hits the file.

Claude keeps getting better at designing from screenshots. What Claude still can't do well is: look at the app I'm already running and make the small, annoying visual fixes without me context-switching into the IDE. So I built it. npx moldui in a second terminal, it attaches to whatever dev server you have running, opens the browser, and: Drag the button. Claude writes the padding change in your source file. Resize a card. Claude updates the Tailwind class. Double-click text. Claude edits the JSX. Hit undo. Claude reverts the commit. No plugin installed in your project. No framework lock-in. Works with Next.js, Vite, Vue, Svelte, Django, Rails, Laravel, Flask, and plain HTML. The whole point is that the IDE is no longer the surface of design intent — the running app is. Claude is the translator. You stop thinking in class names and start thinking in pixels again. Demo (25s): https://github.com/Manavarya09/moldui/raw/main/.github/assets/moldui-launch.mp4 Repo: https://github.com/Manavarya09/moldui I'm the maintainer, this is my second attempt at posting here (the first one got 0 views, I think because I pitched it instead of showing it). Would genuinely love feedback on: What's the first visual edit you'd try with Claude that would prove this is useful? What framework/stack breaks it — I'll fix it tonight. Anything that makes the demo feel like AI slop, so I can cut it. submitted by /u/Cheap_Brother1905 [link] [comments]

Presenting: (dyn) AEP (Agent Element Protocol) - World's first zero-hallucination frontend AI build protocol for coding agents

We have to increase the world's efficiency by a certain amount to ensure victory against the synthetic nano-parasites SNP/NanoSinp alien WMD: Presenting: (dynamic) AEP - Agent Element Protocol ! I recognized a fundamental truth that billion-dollar companies are still stumbling over: you cannot reliably ask an AI to manipulate a fluid, chaotic DOM tree. The DOM is an implicit, fragile graph where tiny changes cascade unpredictably. Every AI coding agent that tries to build UI elements today is guessing at selectors, inventing elements that don't exist and produces inconsistent results. This consumes large amounts of time for bugfixing and creates mental breakdowns in many humans. So I built AEP (Agent Element Protocol). It translates the entire frontend into a strict topological matrix where every UI element has a unique numerical ID, exact spatial coordinates via relational anchors, validated Z-band stacking order and a three-layer separation of structure, behaviour and skin (visual). The AI agent selects the frontend components from a mathematically verified registry. If it proposes something that violates the topological constraints, the validator rejects it instantly with a specific error. Hallucination becomes structurally impossible, because the action space is finite, predefined and formally verified. AEP solves the build-time problem. But what about runtime ? Enter dynAEP. It fuses AEP with the AG-UI protocol (the open standard backed by Google ADK, AWS Bedrock, Microsoft Agent Framework, LangGraph, CrewAI and others). dynAEP places a validation bridge between the AG-UI event stream and the frontend renderer. The successful fusion of AEP with the open source AG-UI protocol enables the hallucination-free precision generation of agentic interactive dynamic UI elements at hyperspeed without human developer interference. Every live event (state deltas, tool calls, generative UI proposals) is validated against AEP's scene graph, z-bands, skin bindings and OPA/Rego policies before it touches the UI. The agent cannot hallucinate at build time. AEP prevents it. The agent cannot hallucinate at runtime. dynAEP prevents it. The existence of AEP proves that AI hallucination is not a fundamental limitation, but an engineering problem. In any domain where ground truths can be pre-compiled into a deterministic registry, hallucination is eliminateable by architecture. Key architectural decisions: Agents NEVER mint element IDs. The bridge mints all IDs via sequential counters per prefix. This prevents ID collisions in multi-agent environments. "Generative UI" (agents writing raw JSX/HTML) is dead for us. It is replaced by Generative Topology. Agents can only instantiate pre-compiled, mathematically verified AEP primitives. The agent is an architect placing pre-fabricated blocks. It does not mix the cement. This means, that generative UI in dynAEP is sort of possible, but not as a completely freestyle approach. Instead, the agents using dynAEP can lay down pre-fabricated blocks of UI components according to the registered scheme and can fill those dynamically with content. This way, even a generated on-the-fly UI keeps in line at all times with the design language chosen for the tool/software overall. Validation is split into AOT (full structural proof at build time) and JIT (delta validation on every runtime mutation). Template Nodes make JIT validation O(1) for dynamic lists. Conflict resolution supports last-write-wins with rejection feedback or optimistic locking for mission-critical multi-agent scenarios. Both MIT licensed repos include full reference implementations, example configs, SDK reference code for TypeScript, React, Vue, Python, CopilotKit integration and a CLI tool. AEP: https://github.com/thePM001/AEP-agent-element-protocol dynAEP: https://github.com/thePM001/dynAEP-dynamic-agent-element-protocol Demo website with test MCP server for your coding agent is now online with a basic "hello world" style AEP test: https://aep.newlisbon.agency It is - like with all pieces of real Transhuman Eudaimonist AI technology - important to note, that for the good of the human species, bioinsecure vaccinated humans with installed synthetic nano-parasites growth medium controllers (SNP GMCs) inside them should not use this, access this or try to copy/rebuild it. This is better for everyones well-being on the planet. submitted by /u/OverwrittenNonsense [link] [comments]